2023 has been a groundbreaking year for Generative AI (Artificial Intelligence) across various fields. By the end of the year, another buzzword has taken center stage, AI PC! What is AI PC? To put it simply, AI PC can be seen as an upgraded PC architecture. It is broadly defined as a computer device with generative AI functions without the need of a cloud platform. It has the same CPU (central processing unit) and GPU (Graphics Processor Unit) as a traditional PC and an additional NPU (Neural Processing Unit) that traditional PCs do not possess. The NPU can quickly process large amounts of data at lower power consumption and is paired with a local LLM (Large Language Model) to further accelerate the development of generative AI in industry, commerce, and education.
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So what is the connection between AI computing, AI PC, and memory? If we compare an AI​​system to a brain, computer memory would equate to the brain&rsquo;s memory areas. Just like the human brain needs to store and retrieve information for learning and decision-making, AI PCs also need memory to store and manage data and model parameters. Since memory is storing data used by large-scale language models, access speed also affects AI computing performance for different tasks. Furthermore, AI PC generally refers to the use of generative AI on a PC without the need to connect the device to the cloud. Even if the PC does not have a network connection, generative AI can still be used on the device.

Therefore, memory plays a vital role in AI PC, directly affecting overall system performance and AI task execution efficiency. AI operation requires a large amount of data for training and deep learning and this data is usually stored in memory temporarily. The trained AI model contains a large number of parameters and as the complexity of the model increases, the number of parameters also increases accordingly, and sufficient memory capacity is required to store these parameters. Thus, high-speed and high-capacity memory can provide faster data access speeds and help speed up the model training process. When importing a trained model into actual applications, optimizing memory, reducing model size, and improving memory usage efficiency is critical.
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In terms of hardware, CPU manufacturers are focusing on the AI processing capabilities of terminal devices in order to pursue faster (immediate response, low latency), safer (local storage) and more economical (reduce server dependence) goals. Whether it is Intel NPU, AMD Ryzen AI, Apple Neural Engine, or Qualcomm NPU, these technologies are dedicated to realizing Client AI applications on terminal devices by integrating neural acceleration engines, meaning users no longer need to rely on cloud computing. AI inference can be performed offline to meet production needs and lay the foundation for the development of AI PCs with efficient computing.
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Since Intel and AMD are expected to launch new platforms in 2024, all new specifications will be equipped with DDR5. This will also accelerate the market share and penetration rate of DDR5, and simultaneously drive the replacement of PC processors. This wave of replacement is expected to drive the vigorous development of AI PCs. Large servers, data centers, and high-end workstations are not the only applications that need high-speed and large-capacity DRAM to achieve faster data access speeds. For those who love new technologies, desire to create through AI, and gamers who have higher requirements for physical speed and capacity, their much anticipated AI experience looks to be within reach!

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2023 has been a groundbreaking year for Generative AI (Artificial Intelligence) across various fields. By the end of the year, another buzzword has taken center stage, AI PC! What is AI PC? To put it simply, AI PC can be seen as an upgraded PC architecture. It is broadly defined as a computer device with generative AI functions without the need of a cloud platform. It has the same CPU (central processing unit) and GPU (Graphics Processor Unit) as a traditional PC and an additional NPU (Neural Pr